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Agent Swarm Benchmarking Tool - Testing Strategy

v2/benchmark/plans/testing-strategy.md

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Agent Swarm Benchmarking Tool - Testing Strategy

๐Ÿงช Test-Driven Development (TDD) Approach

TDD Cycle

  1. RED - Write a failing test
  2. GREEN - Write minimal code to pass the test
  3. REFACTOR - Improve code while keeping tests green
  4. REPEAT - Continue until feature is complete

TDD Benefits

  • Ensures code correctness from the start
  • Provides living documentation
  • Enables confident refactoring
  • Catches regressions early
  • Improves code design

๐Ÿ“‹ Testing Pyramid

                    โ–ฒ
                   / \
                  /   \
                 /  E2E \
                /       \
               /โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€\
              /Integration\
             /             \
            /โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€\
           /      Unit       \
          /                   \
         /โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€\

Test Distribution

  • 70% Unit Tests - Fast, isolated, comprehensive
  • 20% Integration Tests - Component interactions
  • 10% E2E Tests - Complete workflows

๐Ÿ”ฌ Test Categories

1. Unit Tests (tests/unit/)

Test individual components in isolation.

Core Components

python
# tests/unit/core/test_benchmark_engine.py
def test_benchmark_engine_initialization():
    engine = BenchmarkEngine()
    assert engine.status == EngineStatus.READY
    assert engine.task_queue.is_empty()

def test_benchmark_engine_task_submission():
    engine = BenchmarkEngine()
    task = Task(id="1", objective="test", strategy="auto")
    engine.submit_task(task)
    assert len(engine.task_queue) == 1

Strategy Tests

python
# tests/unit/strategies/test_auto_strategy.py
def test_auto_strategy_selection():
    strategy = AutoStrategy()
    task = Task(objective="analyze data", parameters={})
    selected = strategy.select_strategy(task)
    assert selected == "analysis"

def test_auto_strategy_execution():
    strategy = AutoStrategy()
    task = create_test_task()
    result = await strategy.execute(task)
    assert result.status == ResultStatus.SUCCESS

Coordination Mode Tests

python
# tests/unit/modes/test_centralized_mode.py
def test_centralized_mode_agent_assignment():
    mode = CentralizedMode()
    agents = create_test_agents(5)
    tasks = create_test_tasks(10)
    assignments = mode.assign_tasks(agents, tasks)
    assert len(assignments) == 10

2. Integration Tests (tests/integration/)

Test component interactions and data flow.

Strategy-Mode Integration

python
# tests/integration/test_strategy_mode_integration.py
async def test_research_strategy_with_distributed_mode():
    strategy = ResearchStrategy()
    mode = DistributedMode()
    task = create_research_task()
    
    result = await mode.execute_with_strategy(strategy, task)
    assert result.status == ResultStatus.SUCCESS
    assert result.coordination_metrics["overhead"] < 0.1

Output Integration

python
# tests/integration/test_output_integration.py
def test_json_sqlite_consistency():
    results = create_test_results()
    
    json_writer = JSONWriter()
    sqlite_manager = SQLiteManager()
    
    json_data = json_writer.export(results)
    sqlite_manager.store(results)
    
    sqlite_data = sqlite_manager.query_all()
    assert normalize_data(json_data) == normalize_data(sqlite_data)

3. Performance Tests (tests/performance/)

Validate system performance under various conditions.

Load Testing

python
# tests/performance/test_load_handling.py
async def test_high_task_volume():
    engine = BenchmarkEngine()
    tasks = create_test_tasks(1000)
    
    start_time = time.time()
    results = await engine.execute_batch(tasks)
    execution_time = time.time() - start_time
    
    assert len(results) == 1000
    assert execution_time < 60  # Should complete within 1 minute
    assert all(r.status == ResultStatus.SUCCESS for r in results)

Stress Testing

python
# tests/performance/test_resource_limits.py
def test_memory_usage_under_load():
    with resource_monitor() as monitor:
        engine = BenchmarkEngine()
        tasks = create_memory_intensive_tasks(100)
        engine.execute_batch(tasks)
    
    assert monitor.peak_memory < 1024 * 1024 * 1024  # < 1GB

4. End-to-End Tests (tests/e2e/)

Test complete user workflows through the CLI.

CLI Workflow Tests

python
# tests/e2e/test_cli_workflows.py
def test_complete_benchmark_workflow():
    # Execute via CLI
    result = subprocess.run([
        "python", "-m", "swarm_benchmark",
        "run", "test-benchmark",
        "--strategy", "research",
        "--mode", "distributed",
        "--output", "json,sqlite"
    ], capture_output=True, text=True)
    
    assert result.returncode == 0
    assert "Benchmark completed successfully" in result.stdout
    
    # Verify outputs exist
    assert os.path.exists("reports/test-benchmark.json")
    assert os.path.exists("reports/test-benchmark.db")

๐Ÿ› ๏ธ Test Infrastructure

Test Fixtures (tests/fixtures/)

Reusable test data and objects.

python
# tests/fixtures/tasks.py
def create_test_task(**kwargs):
    defaults = {
        "id": str(uuid.uuid4()),
        "objective": "test objective",
        "strategy": "auto",
        "mode": "centralized",
        "timeout": 60,
        "max_retries": 3
    }
    defaults.update(kwargs)
    return Task(**defaults)

def create_test_tasks(count: int) -> List[Task]:
    return [create_test_task(id=str(i)) for i in range(count)]

Mock Objects (tests/mocks/)

Mock external dependencies for isolated testing.

python
# tests/mocks/claude_flow_client.py
class MockClaudeFlowClient:
    def __init__(self):
        self.calls = []
    
    async def execute_swarm(self, objective: str, **kwargs):
        self.calls.append(("execute_swarm", objective, kwargs))
        return MockResult(success=True, output="mock output")

Test Utilities (tests/utils/)

Helper functions for testing.

python
# tests/utils/assertions.py
def assert_result_valid(result: Result):
    assert result.task_id is not None
    assert result.status in [ResultStatus.SUCCESS, ResultStatus.FAILURE]
    assert result.execution_time >= 0
    assert isinstance(result.metrics, dict)

def assert_metrics_complete(metrics: Dict[str, Any]):
    required_keys = ["execution_time", "resource_usage", "quality_score"]
    for key in required_keys:
        assert key in metrics

๐Ÿ“Š Test Coverage Strategy

Coverage Goals

  • Overall Coverage: โ‰ฅ 95%
  • Unit Tests: โ‰ฅ 98%
  • Integration Tests: โ‰ฅ 90%
  • Critical Paths: 100%

Coverage Measurement

bash
# Run tests with coverage
pytest --cov=src --cov-report=html --cov-report=term

# Coverage requirements in pytest.ini
[tool:pytest]
addopts = --cov=src --cov-fail-under=95

Coverage Exclusions

  • Configuration files
  • CLI entry points
  • Error handling for unreachable states
  • Development/debug utilities

๐Ÿ”„ Continuous Testing

Pre-commit Hooks

yaml
# .pre-commit-config.yaml
repos:
  - repo: local
    hooks:
      - id: pytest
        name: pytest
        entry: pytest
        language: python
        stages: [commit]
        types: [python]

GitHub Actions

yaml
# .github/workflows/test.yml
name: Test Suite
on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: [3.8, 3.9, 3.10, 3.11]
    
    steps:
    - uses: actions/checkout@v2
    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: ${{ matrix.python-version }}
    - name: Install dependencies
      run: |
        pip install -r requirements.txt
        pip install -r requirements-dev.txt
    - name: Run tests
      run: pytest --cov=src --cov-report=xml
    - name: Upload coverage
      uses: codecov/codecov-action@v1

๐ŸŽฏ Test Data Management

Test Data Strategy

  • Synthetic Data - Generated test data for consistency
  • Fixtures - Predefined test scenarios
  • Factories - Dynamic test data generation
  • Snapshots - Golden master testing for outputs

Data Generation

python
# tests/data/generators.py
class TaskGenerator:
    @staticmethod
    def simple_task() -> Task:
        return Task(
            id="simple-001",
            objective="Simple test task",
            strategy="auto",
            mode="centralized"
        )
    
    @staticmethod
    def complex_workflow() -> List[Task]:
        return [
            Task(id=f"complex-{i}", objective=f"Step {i}")
            for i in range(10)
        ]

๐Ÿšจ Test Environment Setup

Local Development

bash
# Setup test environment
python -m venv venv
source venv/bin/activate
pip install -r requirements-dev.txt

# Run tests
pytest

# Run with coverage
pytest --cov=src

# Run specific test category
pytest tests/unit/
pytest tests/integration/
pytest tests/performance/

Docker Testing

dockerfile
# Dockerfile.test
FROM python:3.9-slim

WORKDIR /app
COPY requirements*.txt ./
RUN pip install -r requirements-dev.txt

COPY . .
CMD ["pytest", "--cov=src", "--cov-report=html"]

๐Ÿ“ˆ Test Metrics and Reporting

Key Metrics

  • Test Coverage - Code coverage percentage
  • Test Execution Time - Performance of test suite
  • Test Reliability - Flaky test detection
  • Bug Detection - Tests catching real issues

Reporting Tools

  • Coverage.py - Code coverage measurement
  • pytest-html - HTML test reports
  • pytest-benchmark - Performance benchmarking
  • allure-pytest - Advanced test reporting

๐Ÿ” Test Quality Assurance

Test Review Checklist

  • Tests follow naming conventions
  • Tests are independent and isolated
  • Tests have clear assertions
  • Tests cover edge cases
  • Tests are maintainable
  • Tests execute quickly
  • Tests are deterministic

Code Quality Tools

bash
# Linting
flake8 tests/
pylint tests/

# Type checking
mypy tests/

# Security scanning
bandit -r tests/

This comprehensive testing strategy ensures robust, reliable, and maintainable code through systematic test-driven development practices.